So as far as I understand the project, you want to estimate the price of real estate. There're a few ways to do this. Forget pictures for the moment, just go with listed/numeric information.

You have information like Area/Square Footage, Listed Amenities, Age, Location, etc.If you have existing data of this sort, where it lists all the above and then a price, then it is fairly straightforward to pull off – but no guarantees on the accuracy. This has been done by plenty of people, so if you just do this your investors will probably ask you about how you're going to compete with established Real Estate companies who have much bigger teams and much more data.

Now let's consider images: you have pictures of the house, and you want to use those pictures as a way to measure how broken-down/upscale the house is and use that as a parameter to base the price of. You are going to combine this with the above, of course, because it's ridiculous otherwise. I'll say this frankly – this hasn't really been done, and it's a research problem. Not a 'product problem'. You could do a whole PhD thesis on this alone. There are so many different ways to approach this.

• You can use ML to extract furniture from the picture individually, then assign a value to each item of furniture. Aggregate that value to get how well furnished the place.
  • Massive Pitfall - How do you assign a value to a furniture? A minimalistic luxury sofa and an antique cabinet could be worth equally high. Designing this NN would be a huge challenge to start with.
  • Second Pitfall - You need labelled data. You would need a whole team manually annotating the data by looking through hundreds/thousands of furniture images and assigning a value to them.
• You can use ML to determine the quality of the whole room. Forget individual objects, just rate the whole picture from "broken down" to "fancy af" on a scale from 1 to 10 or something.
  • Pitfall - Again, you need labelled data. You'd need a whole team going through images of rooms and marking them. And since you're applying the model into such a very abstracted and broad problem, your results are not really going to be reliable.
• You can use ML at a more micro level. Maybe you could detect broken or damaged furniture.
  • Massive Pitfall - There is very little data available for this, and moreover detecting such issues is still an issue for state of the art models. Some research has been done, such as detecting defects in wooden surfaces and stuff, but it's still at a fairly basic level. Making an algorithm that would detect, say, a crack on a chair, a stain on a cushion, scratches on glass, etc is possible... individually, by zooming in on that thing specifically. Doing this for a whole room on low-mid resolution images would be a nightmare.

Honestly I've given you the entire business plan you're looking for here lmao. Only reason I'm comfortable doing this is because what you're imagining is not really a feasible business plan except for at the very, very basic level.

Like, if you had a team that could pull any of these off, they would be working at AirBnB, Zillow or some other major real estate company already.

If those investors are feeling particularly generous and give you several years and an 7-figure budget, then this might be worth considering. Otherwise...